Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review.

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Uterus motion analysis for radiotherapy planning optimization. The innovative contribution of on-board hybrid MR imaging.

Introduction: Organ motion (OM) and volumetric changes pose challenges in radiotherapy (RT) for locally advanced cervical cancer (LACC). Magnetic Resonance-guided Radiotherapy (MRgRT) combines improved MRI contrast with adaptive RT plans for daily anatomical changes. Our goal was to analyze cervico-uterine structure (CUS) changes during RT to develop strategies for managing OM. Materials and methods: LACC patients received chemoradiation by MRIdian system with a simultaneous integrated boost (SIB) protocol. Prescription doses of 55-50.6 Gy at PTV1 and 45-39.6 Gy at PTV2 were given in 22 and 25 fractions. Daily MRI scans were co-registered with planning scans and CUS changes were assessed.Six PTVs were created by adding 0.5, 0.7, 1, 1.3, 1.5, and 2 cm margins to the CUS, based on the simulation MRI. Adequate margins were determined to include 95 % of the CUSs throughout the entire treatment in 95 % of patients. Results: Analysis of 15 LACC patients and 372 MR scans showed a 31 % median CUS volume decrease. Asymmetric margins of 2 cm cranially, 0.5 cm caudally, 1.5 cm posteriorly, 2 cm anteriorly, and 1.5 cm on both sides were optimal for PTV, adapting to CUS variations. Post-14th fraction, smaller margins of 0.7 cm cranially, 0.5 cm caudally, 1.3 cm posteriorly, 1.3 cm anteriorly, and 1.3 cm on both sides sufficed. Conclusion: CUS mobility varies during RT, suggesting reduced PTV margins after the third week. MRgRT with adaptive strategies optimizes dose delivery, emphasizing the importance of streamlined IGRT with reduced PTV margins using a tailored MRgRT workflow with hybrid MRI-guided systems.

Delta radiomics: an updated systematic review.

BACKGROUND: Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS: Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool. RESULTS: Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS: Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.

An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators.

BACKGROUND: Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black-boxes due to the difficulty of understanding the decision-making process used by the algorithm to obtain a specific result. AIMS: For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis. MATERIALS & METHODS: Since the diagnostic task was a three-class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme. RESULTS: The accuracy of the three-class model reaches an overall accuracy of 86.36%, and the precision per-class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. DISCUSSION: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system. CONCLUSIONS: This is the first work that attempts to design an explainable machine-learning tool for the histological diagnosis of solid masses of the ovary.

Radiomics of Intrahepatic Cholangiocarcinoma and Peritumoral Tissue Predicts Postoperative Survival: Development of a CT-Based Clinical-Radiomic Model.

BACKGROUND: For many tumors, radiomics provided a relevant prognostic contribution. This study tested whether the computed tomography (CT)-based textural features of intrahepatic cholangiocarcinoma (ICC) and peritumoral tissue improve the prediction of survival after resection compared with the standard clinical indices. METHODS: All consecutive patients affected by ICC who underwent hepatectomy at six high-volume centers (2009-2019) were considered for the study. The arterial and portal phases of CT performed fewer than 60 days before surgery were analyzed. A manual segmentation of the tumor was performed (Tumor-VOI). A 5-mm volume expansion then was applied to identify the peritumoral tissue (Margin-VOI). RESULTS: The study enrolled 215 patients. After a median follow-up period of 28 months, the overall survival (OS) rate was 57.0%, and the progression-free survival (PFS) rate was 34.9% at 3 years. The clinical predictive model of OS had a C-index of 0.681. The addition of radiomic features led to a progressive improvement of performances (C-index of 0.71, including the portal Tumor-VOI, C-index of 0.752 including the portal Tumor- and Margin-VOI, C-index of 0.764, including all VOIs of the portal and arterial phases). The latter model combined clinical variables (CA19-9 and tumor pattern), tumor indices (density, homogeneity), margin data (kurtosis, compacity, shape), and GLRLM indices. The model had performance equivalent to that of the postoperative clinical model including the pathology data (C-index of 0.765). The same results were observed for PFS. CONCLUSIONS: The radiomics of ICC and peritumoral tissue extracted from preoperative CT improves the prediction of survival. Both the portal and arterial phases should be considered. Radiomic and clinical data are complementary and achieve a preoperative estimation of prognosis equivalent to that achieved in the postoperative setting.

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Coping with modesty during radiotherapy for breast cancer: a multicentric study.

PURPOSE: Breast cancer is the most frequently diagnosed tumour, representing nearly 30% of all new cases in women. Radiotherapy (RT) plays a crucial role in the management of breast cancer. The objective of this study is to assess modesty in patients undergoing RT for breast cancer and take their suggestions and ideas into consideration to enhance the quality of treatment in this regard. METHODS: The study enrolled 555 breast cancer patients undergoing adjuvant RT in three Italian centres. Patients completed a self-test questionnaire assessing their comfort level concerning modesty during therapy and their relationship with strangers and healthcare professionals. The impact of religious views and potential changes in sexuality were also examined. RESULTS: Results showed that modesty was a common concern across the overall cohort of patients, with discomfort in being undressed during RT correlating with discomfort experienced in other daily life situations. Most patients felt more at ease with same sex healthcare workers. Age was also a major factor with younger patients generally feeling more comfortable with healthcare workers of the same age group. Interestingly, the surgical technique used (mastectomy vs. quadrantectomy) did not significantly influence modesty perceptions. Patients provided valuable suggestions to improve privacy and modesty during RT. CONCLUSION: This study demonstrates that modesty is an important issue for women undergoing RT, which can be influenced by personal characteristics and hospital-related factors. A reflection about the need to address modesty concerns and to incorporate dedicated interventions for protecting patients' physical and emotional well-being is warranted. Initiatives to improve communication, involvement, and body image support should also be integrated into the care path of patients to better their overall therapeutic experience. This study paves the way for broader research and interventions in daily cancer care.

Stereotactic Radiotherapy for penile metastasis: case report and systematic literature review.

Introduction Penile metastases (PM) are a rare clinical presentation mainly related to advanced stages of disease. Considering the low incidence, an optimal treatment approach has not yet been defined; surgery, chemotherapy, and radiotherapy are different options used in the vast majority with palliative intent. The advances in modern RT can represent an innovative tool in PM management and a curative option. This paper aims to report the case of a PM patient treated with Stereotactic Body Radiotherapy (SBRT) and perform a systematic literature review of current evidence on the RT approach to PM. Case report We reported the case of an 80-year-old patient with PM from primary bladder cancer. Following the surgical approach for the primary tumor, evidence of PM was shown, and the patient was admitted to SBRT treatment on PM after an adjuvant RT course on the pelvis. A 25 Gy in 5 fractions SBRT treatment was performed, and a complete clinical response was shown at the first follow-up. Methods A Pubmed/MEDLINE and Embase systematic review was carried out. The search strategy terms were [('penile metastasis'/exp OR 'penile metastasis' OR (penile AND ('metastasis'/exp OR metastasis))) AND ('radiotherapy'/exp OR radiotherapy)] and only original articles up to the 24.10.2023 were considered. Results A total of 174 studies were obtained using the previously mentioned search strategy, and the analysis was performed on 15 papers obtained following the complete selection process. All reported evidence was focused on the palliative approach of PM showing good results in terms of symptom control. Discussion The potential role of modern RT in the management of PM has yet to be defined. The reported case showed the feasibility and the clinical impact of SBRT in PM treatment.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

REPeated mAgnetic resonance Image-guided stereotactic body Radiotherapy (MRIg-reSBRT) for oligometastatic patients: REPAIR, a mono-institutional retrospective study.

BACKGROUND: Oligo-progression or further recurrence is an open issue in the multi-integrated management of oligometastatic disease (OMD). Re-irradiation with stereotactic body radiotherapy (re-SBRT) technique could represent a valuable treatment option to improve OMD clinical outcomes. MRI-guided allows real-time visualization of the target volumes and online adaptive radiotherapy (oART). The aim of this retrospective study is to evaluate the efficacy and toxicity profile of MRI-guided repeated SBRT (MRIg-reSBRT) in the OMD setting and propose a re-SBRT classification. METHODS: We retrospectively analyzed patients (pts) with recurrent liver metastases or abdominal metastatic lesions between 1 and 5 centimeters from liver candidate to MRIg-reSBRT showing geometric overlap between the different SBRT courses and assessing whether they were in field (type 1) or not (type 2). RESULTS: Eighteen pts completed MRIg-reSBRT course for 25 metastatic hepatic/perihepatic lesions from July 2019 to January 2020. A total of 20 SBRT courses: 15 Type 1 re-SBRT (75%) and 5 Type 2 re-SBRT (25%) was delivered. Mean interval between the first SBRT and MRIg-reSBRT was 8,6 months. Mean prescribed dose for the first treatment was 43 Gy (range 24-50 Gy, mean BEDα/ß10=93), while 41 Gy (range 16-50 Gy, mean BEDα/ß10=92) for MRIg-reSBRT. Average liver dose was 3,9 Gy (range 1-10 Gy) and 3,7 Gy (range 1,6-8 Gy) for the first SBRT and MRIg-reSBRT, respectively. No acute or late toxicities were reported at a median follow-up of 10,7 months. The 1-year OS and PFS was 73,08% and 50%, respectively. Overall Clinical Benefit was 54%. CONCLUSIONS: MRIg-reSBRT could be considered an effective and safe option in the multi-integrated treatment of OMD.

Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs' auto-segmentation and visual grading assessment.

Purpose: Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance. Materials and methods: 3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett's S score and Fleiss' kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively. Results: In the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs' boundaries compared with the original MRI. Conclusion: The bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs' auto-segmentation outputs generated by the GAN.

Impact of data transfer between treatment planning systems on dosimetric parameters.

PURPOSE: In radiotherapy it is often necessary to transfer a patient's DICOM (Digital Imaging and COmmunications in Medicine) dataset from one system to another for re-treatment, plan-summation or registration purposes. The aim of the study is to evaluate effects of dataset transfer between treatment planning systems. MATERIALS AND METHODS: Twenty-five patients treated in a 0.35T MR-Linac (MRidian, ViewRay) for locally-advanced pancreatic cancer were enrolled. For each patient, a nominal dose distribution was optimized on the planning MRI. Each plan was daily re-optimized if needed to match the anatomy and exported from MRIdian-TPS (ViewRay Inc.) to Eclipse-TPS (Siemens-Varian). A comparison between the two TPSs was performed considering the PTV and OARs volumes (cc), as well as dose coverages and clinical constraints. RESULTS: From the twenty-five enrolled patients, 139 plans were included in the data comparison. The median values of percentage PTV volume variation are 10.8 % for each fraction, while percentage differences of PTV coverage have a mean value of -1.4 %. The median values of the percentage OARs volume variation are 16.0 %, 7.0 %, 10.4 % and 8.5 % for duodenum, stomach, small and large bowel, respectively. The percentage variations of the dose constraints are 41.0 %, 52.7 % and 49.8 % for duodenum, stomach and small bowel, respectively. CONCLUSIONS: This study has demonstrated a non-negligible variation in size and dosimetric parameters when datasets are transferred between TPSs. Such variations should be clinically considered. Investigations are focused on DICOM structure algorithm employed by the TPSs during the transfer to understand the cause of such variations.

A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.

Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is ()often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined can help overcome these issues, as one provides the means of training models without exchanging sensitive data, while the other identifies the most informative features, reduces overfitting, and improves model interpretability. Our proposed FS pipeline based on FL principles targets data-driven radiomics FS in a multivariate survival study of non-small cell lung cancer patients. The pipeline was run across datasets from three institutions without patient-level data exchange. It includes two FS techniques, Correlation-based Feature Selection and LASSO regularization, and Cox Proportional-Hazard regression with Overall Survival as endpoint. Trained and validated on 828 patients overall, our pipeline yielded a radiomic signature comprising "intensity-based energy" and "mean discretised intensity". Validation resulted in a mean Harrell C-index of 0.59, showcasing fair efficacy in risk stratification. In conclusion, we suggest a distributed radiomics approach that incorporates preliminary feature selection to systematically decrease the feature set based on data-driven considerations. This aims to address dimensionality challenges beyond those associated with data constraints and interpretability concerns.

Factors influencing local control after MR-guided stereotactic body radiotherapy (MRgSBRT) for adrenal metastases.

Purpose: Stereotactic body radiotherapy (SBRT) is an effective treatment for adrenal gland metastases, but it is technically challenging and there are concerns about toxicity. We performed a multi-institutional pooled retrospective analysis to study clinical outcomes and toxicities after MR-guided SBRT (MRgSBRT) using for adrenal gland metastases. Methods and Materials: Clinical and dosimetric data of patients treated with MRgSBRT on a 0.35 T MR-Linac at 11 institutions between 2016 and 2022 were analyzed. Local control (LC), local progression-free survival (LPFS), distant progression-free survival (DPFS) and overall survival (OS) were estimated using Kaplan-Meier method and log-rank test. Results: A total of 255 patients (269 adrenal metastases) were included. Metastatic pattern was solitary in 25.9 % and oligometastatic in 58.0 % of patients. Median total dose was 45 Gy (range, 16-60 Gy) in a median of 5 fractions, and the median BED10 was 100 Gy (range, 37.5-132.0 Gy). Adaptation was done in 87.4 % of delivered fractions based on the individual clinicians' judgement. The 1- and 2- year LPFS rates were 94.0 % (95 % CI: 90.7-97.3 %) and 88.3 % (95 % CI: 82.4-94.2 %), respectively and only 2 patients (0.8 %) experienced grade 3 + toxicity. No local recurrences were observed after treatment to a total dose of BED10 > 100 Gy, with single fraction or fractional dose of > 10 Gy. Conclusions: This is a large retrospective multi-institutional study to evaluate the treatment outcomes and toxicities with MRgSBRT in over 250 patients, demonstrating the need for frequent adaptation in 87.4 % of delivered fractions to achieve a 1- year LPFS rate of 94 % and less than 1 % rate of grade 3 + toxicity. Outcomes analysis in 269 adrenal lesions revealed improved outcomes with delivery of a BED10 > 100 Gy, use of single fraction SBRT and with fraction doses > 10 Gy, providing benchmarks for future clinical trials.

Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.

PURPOSE: The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. METHODS: Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. RESULTS: A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). CONCLUSION: The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models.

PURPOSE: Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS: 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients. RESULTS: In the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient. CONCLUSIONS: The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.

Evaluation of clinical parallel workflow in online adaptive MR-guided Radiotherapy: A detailed assessment of treatment session times.

Introduction: Advancements in MRI-guided radiotherapy (MRgRT) enable clinical parallel workflows (CPW) for online adaptive planning (oART), allowing medical physicists (MPs), physicians (MDs), and radiation therapists (RTTs) to perform their tasks simultaneously. This study evaluates the impact of this upgrade on the total treatment time by analyzing each step of the current 0.35T-MRgRT workflow. Methods: The time process of the workflow steps for 254 treatment fractions in 0.35 MRgRT was examined. Patients have been grouped based on disease site, breathing modality (BM) (BHI or FB), and fractionation (stereotactic body RT [SBRT] or standard fractionated long course [LC]). The time spent for the following workflow steps in Adaptive Treatment (ADP) was analyzed: Patient Setup Time (PSt), MRI Acquisition and Matching (MRt), MR Re-contouring Time (RCt), Re-Planning Time (RPt), Treatment Delivery Time (TDt). Also analyzed was the timing of treatments that followed a Simple workflow (SMP), without the online re-planning (PSt + MRt + TDt.). Results: The time analysis revealed that the ADP workflow (median: 34 min) is significantly (p < 0.05) longer than the SMP workflow (19 min). The time required for ADP treatments is significantly influenced by TDt, constituting 40 % of the total time. The oART steps (RCt + RPt) took 11 min (median), representing 27 % of the entire procedure. Overall, 79.2 % of oART fractions were completed in less than 45 min, and 30.6 % were completed in less than 30 min. Conclusion: This preliminary analysis, along with the comparative assessment against existing literature, underscores the potential of CPW to diminish the overall treatment duration in MRgRT-oART. Additionally, it suggests the potential for CPW to promote a more integrated multidisciplinary approach in the execution of oART.